Quality profiles representing the quality of a process or product as the functional relationship between one or more dependent variables and one or more explanatory variables are nowadays widely recognized in statistical process control (SPC) applications by both researchers and practitioners. On the other hand, in many real-world cases, evaluation of process or product characteristics is carried out with ambiguity or conducted using linguistic values. The theory of fuzzy sets provides an appropriate approach to deal with uncertainty due to ambiguity in human subjective evaluations or vagueness in linguistic variables. The purpose of this study is to introduce two novel methods based on fuzzy regression modeling for monitoring fuzzy linear profiles in phase II of SPC. To accomplish this, fuzzified Hoteling’s T2 statistic and fuzzy hypothesis testing are used. Moreover, a simulation study is used to compare the performance of the proposed methods compared with previous methods, based on the average run length (ARL) criterion in order to assess the detectability of charts with regard to the step shifts in profile parameters. Finally, the results of a real-world example in the tile and ceramic industry are presented.

The behavior of clay minerals in expansive soils causes them to exhibit shrink-swell characteristics, making them unsuitable for engineering purposes in their natural state. To address this problem, researchers conducted direct shear experiments using fly ash as an admixture and black cotton soil as an expanding soil to explore the strength parameter. The experiments were conducted with varying amounts of fly ash ranging from 2% to 20%. Two arrangements of test series were made, and in the principal series, tests were made utilizing five unique densities and comparing dampness contents. The outcomes showed that the point of inside grating and union expanded directly up to the ideal dampness content and most significant dry thickness before diminishing. The subsequent series showed that the end of inward rubbing grew straightly with the expansion of fly debris admixture, yet attachment reduced after 10% admixture. The decrease in shear strength was because of the diminished passion, as the fly debris' cohesionless attributes took over as the admixture rate increased above 10%. Based on these findings, adding fly ash in small quantities to black cotton soil is recommended to avoid weakening it.

In this article, the application of the Multi-Skilled Resource-Constrained Flow Shop Scheduling Problem (MSRC-FSSP) in preventive maintenance as a case study has been investigated. In other words, to complete each maintenance order at each stage, in addition to the machine, a set of required human resources with different skills must be available. According to human resources skills, each of them can perform at least one order or at most N orders, and each maintenance order must be done by a set of human resources with different skills. To carry out a maintenance order, different human resources must be in communication and cooperation so that a preventive maintenance order can be completed. In this article, these resources are considered as technical supervisors, repairmen and maintenance managers who complete all maintenance orders in a flow shop environment as a job. For this problem, a new Mixed Integer Linear Programming (MILP) model has been formulated with the two-objective functions, minimizing total orders completion time and the human resources idle time. To solve the model on a small scale, CPLEX is used, and to solve it on a large scale, due to the fact that this problem is NP-Hard, a meta-heuristic algorithm named Genetic Algorithm (GA) is presented. Finally, the computational results have been done to validate the model, along with the analysis of the human resources idle time.

The COVID-19 pandemic caused significant changes in many aspects especially towards small medium entrepreneurs (SMEs) as many of SMEs need to shut down their business due to movement control order (MCO) conducted by Malaysia government as SMEs cannot reach their customers. Previous studies show that having an effective digital marketing strategy in place might leave businesses vulnerable to severe setbacks towards SMEs. This research proposal aims to carry out and prove the possible potential effects and factors that influence digital marketing implementation towards online consumers in Selangor during COVID-19 pandemic. The methodology of this paper uses a descriptive qualitative approach by analyzing various previous literature on digital marketing scope of study. The sample size of the study is 235 respondents who were selected based on convenience sampling. The finding has discovered that there is a significant relationship between customer loyalty with implementation of digital marketing towards online consumers in Selangor during COVID-19 pandemic and there is a significance relationship between brand awareness and the implementation of digital marketing towards online consumer in Selangor during COVID-19 pandemic.

Despite the numerous advantages that different mobile payments can provide, their acceptance, and adoption rates are still relatively low. This study aims at investigating mobile payments and demonstrates how drivers and barriers that influence behavioral intentions to use mobile payments interact and support one another by combining the theory of planned behavior (TPB) and the innovation resistance theory (IRT). A self-administered online survey was employed to gather data from 341 users of mobile payments in the State of Kuwait. To test the proposed model and its hypotheses, responses were analyzed using a partial least square structural equation modeling approach (PLS-SEM). The results show that usage, value, risk, and tradition resistance-related factors are significant barriers towards behavioral intentions to use mobile payments, while the image barrier is insignificant. The findings also affirmed that perceived behavioral control and attitudes motivate and influence consumers’ behavioral intentions; however, the subjective norm was non-significant. The study’s findings have significant implications for scholars, mobile payments’ service providers, marketers, policymakers, and banks.

Heavy metals contamination of water is a serious and complex environmental problem due to rapid industrialization, bioaccumulation and non-degradability. Therefore, the reuse of agricultural waste in the process of purifying water from pollutants is an attractive and promising method. Almost inexpensive materials are used to purify water, thus achieving the desired economic and environmental goal. In this study banana peel (BP) was used before and after modification by 0.1M sulphuric acid (H2SO4) to enhance the removal of Zinc Zn(II), Lead Pb(II) and Nickel Ni(II). The effect of various parameters was studied Such as pH, contact time, adsorbent dosage and initial metal ion concentration at 5 ppm. All these parameters were studied in batch experiments for a comparative study. The removal percentage was found to be 84% Zn (II),78%Pb (II) and 72% Ni (II) before modification and 92% Zn (II) , 94% Pb (II) and 96% Ni (II) after modification at initial concentration of 5 ppm. The data obtained from sorption isotherms were described with Langmuir and Freundlich isotherm models but were found to be well fitted for the Langmuir model. The correlation coefficient values R2 for Langmuir were at the range (0.996-0.999) while that for Freundlich were at the range (0.912-0.972). The rate of adsorption follows Pseudo-second-order kinetics. This work proved the high banana peels efficiency as an adsorbent agent for heavy metals removal from aqueous solutions.

The Jaya algorithm is a recently developed novel population-based algorithm. The proposed work presents the modifications in the existing many-objective Jaya (MaOJaya) algorithm by integrating the chaotic sequence to improve the performance to optimize many-objective benchmark optimization problems. The MaOJaya algorithm has exploitation more dominating, due to which it traps in local optima. The proposed work aims to reduce these limitations by modifying the solution update equation of the MaOJaya algorithm. The purpose of the modification is to balance the exploration and exploitation, improve the divergence and avoid premature convergence. The well-known chaotic sequence - a logistic map integrated into the solution update equation. This modification keeps the MaOJaya algorithm simple as well as, preserves its parameterless feature. The other component of the existing MaOJaya algorithm, such as non-dominated sorting, reference vector and tournament selection scheme of NSGA-II is preserved. The decomposition approach used in the proposed approach simplifies the complex many-objective optimization problems. The performance of the proposed chaotic based many-objective Jaya (C-MaOJaya) algorithm is tested on DTLZ benchmark functions for three to ten objectives. The IGD and Hypervolume performance metrics evaluate the performance of the proposed C-MaOJaya algorithm. The statistical tests are used to compare the performance of the proposed C-MaOJaya algorithm with the MaOJaya algorithm and other algorithms from the literature. The C-MaOJaya algorithm improved the balance between exploration and exploitation and avoids premature convergence significantly. The comparison shows that the proposed C-MaOJaya algorithm is a promising approach to solve many-objective optimization problems.

Since the late 80’s the Structural Analysis Research Group of the Instituto Superior Técnico (IST) has been involved in the development of non-conventional finite element formulations in order to overcome some of the limitations associated with the use of the CFE method and to develop high performance numerical tools for the analysis of structural engineering problems. Several alternative models for the linear and non-linear structural analysis have been developed using hybrid and mixed models techniques. These works are summarized in this paper, in which their past and future applications of this formulation in non-linear analysis of structures are fully detailed.

Management scholars view workplace spirituality as the main factor behind building trust among employees and playing a pivotal role in enhancing the organization's positive outcomes, i.e., knowledge sharing behavior & work engagement. Underpinning social exchange theory, we explored the linkage between workplace spirituality, knowledge sharing behavior, and work engagement. We further studied to look at the mediating effect of trust between workplace spirituality and positive outcomes. Data was collected from six private companies, the total number of respondents was (n=196). The study's analysis showed that workplace spirituality substantially positively impacts knowledge sharing behavior and work engagement. Furthermore, the link between workplace spirituality, knowledge sharing behavior, and work engagement is positively and statistically significantly mediated by trust. Thus, this work contributes significantly to the research paradigm by presenting workplace spirituality as a solution for high-rise trust among employees, fostering employee engagement in their work, and improving the capacity of knowledge-sharing behavior. Additionally, at the end of this study, theoretical and managerial suggestions, future avenues, and limitations are stated.

The Internet of Things (IoT) has become essential for business. The adoption rate of IoT has dropped recently and this could be due to security, privacy, and trust issues. Blockchain (BC) has the potential to mitigate the risk of security, privacy, and trust. However, few studies examined the integration between IoT and BC in the context of developing countries. The purpose of this study is to examine the predictors of IoT adoption by telecommunication companies in the Gulf Cooperation Council (GCC). In addition, the study aims to examine the moderating role of BC as well as the effect of using IoT and BC on the competitive advantage of companies. Based on technology acceptance model, social exchange theory, and resource-based view, the study proposed that security, privacy, trust, communication quality, perceived ease of use (PEOU), and perceived usefulness (PU) affect positively the adoption of IoT. BC is proposed as a moderating variable and expected with IoT to affect the competitive advantage of companies. The population includes all the telecommunication companies in GCC. Data was collected using purposive sampling from IT professionals. The results of data analysis using SmartPLS showed that security, privacy, trust, PU, and PEOU positively affected the adoption of IoT. BC and IoT adoption have a positive effect on competitive advantage. Further, BC moderated only the effect of security and privacy on the adoption of IoT. Services providers must enhance the security, privacy, and trust of IoT services by deploying BC technology. Effective integration of IoT and BC will lead to the achievement of competitive advantages.